Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2011-2012C O N T E N TS
■ Message from Director-General1■ Institute Overview

2■ Research Centers

5■ Research Cooperation
International Cooperation

16

Research Collaboration

16■ Graduate School Program17■ Outreach Activities
Tutorial Courses

20

Statistical Mathematics Seminar Series 21
Open Lecture

22

Consultation about Statistical Science 22
Annual Research Meeting22■ Facilities and Equipment

23■ Finance and Buildings24■ Organization25■ History

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Message from Director-General
Message from Director-General

The Institute of Statistical Mathematics is a well-established research
institute founded in 1944 during the Second World War. Following theretirement of former Director-General Dr. Kitagawa, I became theInstitute’s eleventh Director-General on April 1, 2011 amidst a turbulent
research environment caused by the largest ever natural disaster inJapanese history. It is still unknown how much impact the current
pervasive sense of uncertainty about Japan’s future will have on society
and academia. When I consider a role that the Institute should fulfill as
an inter-university research organization under these circumstances,I am both sobered and filled with resolve at the thought of the heavy
responsibilities of this office.
In 2011, the second year of the second medium-term period sinceincorporation, the Institute of Statistical Mathematics is steadily
implementing the Network Of Excellence (NOE) project and a project
to foster statistical thinking, and engaging in advanced research as
Japan's foremost institute for statistical mathematics research andeducation. With regard to the NOE project, we plan to launch a new
strategic research center that will become a nucleus for the pursuitof research in machine learning and service science. In the project
to foster statistical thinking, we plan to strengthen collaboration
with other organizations involved with mathematical science andfurther increase our contribution to the fostering and development ofprofessionals with skills essential in today’s era of large-scale data.
Statistical mathematics is a science that deals with research on
methodologies to appropriately link scattered and unevenly distributed
pieces of information, allowing rational prediction based on data using
mathematics as a tool. Accordingly, it is no exaggeration to say that statistical mathematics performs a role in connecting free and flexi­ble
ideas of researchers to the real world. The Institute of Statistical Mathematics utilizes this connective role to link researchers from different
academic disciplines and wide-ranging research fields and to connect
university to university, industry to academia, and academia with society
at large.
We live in an age of globalism that has brought not only unexpectedchanges in the natural environment, but also a tremendous increasein the uncertainty of human affairs, including financial and economic
crises and dramatic changes in the international balance of power.
Our aim is to create new research fields by clearly recognizing andaccepting the expectations of society and fully harnessing the con­nective role of the Institute, and to contribute to the revitalization ofJapan by connecting people with each other. We would appreciate yourcontinued understanding and support for the activities of the Instituteof Statistical Mathematics.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling

Institute Overview

The Department of Statistical Modeling conducts research on the modeling of causally, temporally and/or
spatially interrelated complex phenomena, including intelligent information processing systems. It also conducts
researches on model-based statistical inference methodologies.

■ Spatial and Time Series Modeling Group —————————————————————————————————————The Spatial and Time Series Modeling Group works on modeling and inference for the statistical analysis of time series,
spatial, and space-time data, and on their applications to prediction and control.

■ Intelligent Information Processing Group —————————————————————————————————————The Intelligent Information Processing Group works on concepts and methods for the extraction, processing andtransformation of information in intelligent systems, motivated by an active interest in practical problems in engineeringand science.

■ Graph Modeling Group ————————————————————————————————————————————————The Graph Modeling Group works on analyses of the data generated by systems with a graph structure and on themodeling required in order to reconstruct the original system.

Department of Data Science
The Department of Data Science aims to develop research methods for surveys, multidimensional data
analyses, and computational statistics.

■ Survey Research Group ————————————————————————————————————————————————The Survey Research Group focuses on research related to statistical data collection and data analyses.

■ Multidimensional Data Analysis Group ———————————————————————————————————————The Multidimensional Data Analysis Group studies methods for analyzing phenomena grasped on multidimensional space
and ways for collecting multidimensional data.

■ Computational Statistics Group ———————————————————————————————————————————The Computational Statistics Group studies sophisticated uses of computers in statistical methodology such as computerintensive data analyses, computational scientific methods, and statistical systems.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization theory, and the practice of statistics in science.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical theory and probability theory that havestatistical applications.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies that enable researchers to learn from data sets andto properly extract information through appropriate inference procedures.

■ Computational Mathematics Group ————————————————————————————————————————The Computational Mathematics Group studies computational algorithms as well as mathematical methodologies used
for statistical modeling and inference.

Strategic ResearchPrediction and Knowledge Discovery Research Center
The Prediction and Knowledge Discovery Research Center studies the statistical modeling and inference algorithms
that can be used to extract useful information from the huge amount of data which complex systems produce, and thus
attempts to solve real-world problems in many different scientific domains, especially genomics, earth and space sciences.

■ Molecular Evolution Research Group ————————————————————————————————————————The Molecular Evolution Research Group researches the area of molecular phylogenetics, and seeks to develop statisticalmethods for inferring evolutionary trees of life using DNA and protein sequences.

2

Strategic Research■ Statistical Seismology Research Group ——————————————————————————————————————The Statistical Seismology Research Group is concerned with the evaluation of seismicity anomalies, detection of crustal
stress changes, their modeling, and the probability forecasting of large aftershocks and earthquakes.

Institute Overview■ Statistical Genome Diversity Research Group ——————————————————————————————————The Statistical Genome Diversity Research Group aims to construct novel methodologies for learning and inference froma variety of data sets in the rapidly growing area of bioinformatics.

Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the study of the increased uncertainty
and risk associated with the increasing globalization of the society and economy. The center is also constructing
a network for risk analysis in order to contribute to the creation of a reliable and safe society.

■ Food and Drug Safety Research Group ——————————————————————————————————————The Food and Drug Safety Research Group aims to develop the statistical framework and methodology of quantitative risk
evaluation for substances ingested by the human body.

■ Environmental Risk Research Group ————————————————————————————————————————The Environmental Risk Research Group studies the statistical methodologies related to environmental risk andenvironmental monitoring.

■ Financial Risk and Insurance Research Group ——————————————————————————————————The Financial Risk and Insurance Research Group explores the use of statistical modeling methods to quantify the risks
involved with financial instruments and insurance products.

■ Research Group for Reliability and Quality Assurance of Service and Product ———————————————The Research Group for Reliability and Quality Assurance of Service and Product aims to improve the safety of products
and services by developing statistical methods that contribute to quality assurance and reliability and by promoting the
adoption of these methods in the industrial world.

Research Innovation Center
The objective of this center is to establish innovative research in statistical mathematics to keep up with new
trends in the academic and real world. The center carries out original research projects, ranging over both pureand applied frontiers.

■ Functional Analytic Inference Research Group ——————————————————————————————————
This group aims to develop nonparametric methodology for statistical inference using reproducing kernel Hilbert spaces
given by positive definite kernels, and to apply these techniques to causal inference problems.

■ Advanced Monte Carlo Algorithm Research Group ———————————————————————————————The Advanced Monte Carlo Algorithm Research Group aims to develop Markov Chain Monte Carlo and Sequential Monte
Carlo algorithms and study their applications.

■ Speech and Music Information Research Group —————————————————————————————————The Speech and Music Information Research Group investigates novel information retrieval methods using machinelearning from time series data, including speech, music, and brain data.

■ Optimization-based Inference Research Group —————————————————————————————————
Optimization-based Inference Research Group focuses on optimization methodology as a fundamental tool forcomputational inference and aims to develop new inference techniques in statistical applications.

Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict
the future and to produce designs for effective observation systems by means of “data assimilation”, which is afundamental technology integrating numerical simulations and observational data.

■ Data Assimilation Project ———————————————————————————————————————————————
Theoretical studies of sequential Bayesian filters that update simulation models by referring to observation data, andapplications of data assimilation to various fields of science.

■ Physical Random Number Generation ———————————————————————————————————————

Implementation of technologies related to rapid generation of high-quality physical random numbers, essential in statisticalcomputations including data assimilation.

3

Institute Overview						

Strategic Research
Research and Development Center for Data Assimilation■ Statistical Analysis on Massively Parallel Computers ——————————————————————————————Development of platforms to conduct statistical analyses in the R language on massively parallel computers.

Institute Overview■ Cloud Computing Services ——————————————————————————————————————————————
Provision of cloud computing services in which statistical computations using parallel programs developed in the center
are available through the internet.

■ Visualization Software Project ————————————————————————————————————————————
3-D visualization software developed in order to demonstrate results of data assimilation as well as to give hints leading
to better simulation models.

Survey Science Center
Founded on the accomplishments in social research by the Institute of Statistical Mathematics spanningover half a century including the Study of the Japanese National Character and the cross-national comparative
research on national characteristics, the Survey Science Center was established in January of 2011 in orderto facilitate further growth of the aforementioned sets of research as well as the establishment of networking
ties with both domestic and international research organizations and the increase in the capacity to makecontributions to wider society by creating what we call the NOE (Network Of Excellence).

■ The Study of the Japanese National Character ( JNC ) ——————————————————————————————The longitudinal nationwide survey has been carried out since 1953 every 5 years with the purpose of clarifying theJapanese national character.  This study shows some stable aspects such as human relationships in Japan, as well assome other aspects changing over years with the changes of economic, political and social conditions.

■ The Cross-national Studies of the National Character —————————————————————————————The JNC survey has been developed into the cross-national comparative surveys which cover the people with Japanese
ancestry overseas since 1971.  This study attempts to understand the Japanese people and their culture in the comparative
context as well as the global configuration of psychological distances of many countries (a sort of cultural manifold).

■ The Project on Accumulating Information on Social Research —————————————————————————
Many data sets of our past surveys in various fields have been accumulated.  These are being organized as a database
open to researchers in the ISM collaboration studies, and to public eventually.

■ The Project on Collaborative Experimental Survey Research —————————————————————————
In collaborations with universities or institutes, we carry out experimental survey research on various topics.  We expect
many young researchers to experience practical surveys through our efforts, including statistical sampling, data-cleaningand data analyses.

■ The Project on Utilizing Information on Social Research ————————————————————————————
Under our paradigm “Science of Data”, we study practical and scientific ways to utilize survey data and develop new
statistical methods and techniques to collect and analyze survey data.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

■ Computing Facilities Unit ———————————————————————————————————————————————The Computing Facilities Unit is in charge of managing computer facilities, software for research, networking infrastructureand network security.

■ Information Resources Unit —————————————————————————————————————————————The Information Resources Unit is responsible for maintaining an extensive library and an electronic repository, and is incharge of planning statistical education courses to popularize research results.

■ Media Development Unit ———————————————————————————————————————————————The Media Development Unit is in charge of the publication and editing of research results and is responsible for public relations.

4Network Of Excellence

NOE Establishing Project for Statistical Mathematics:
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
Basic Research and Strategic Research Framework

Strategic Research Centers
The strategic research centers pursue research activities
that interface statistical mathematics with individual scientificdisciplines in order to find solutions to real and urgent social
problems. The strategic research centers are staffed bypermanent researchers within the Institute, together withvisiting professors, post-doctoral staff, and visiting researchers.
The Institute launched two new centers on January 1, 2011,
in the previous fiscal year, in addition to the existing Prediction and Knowledge Discovery Research Center and the Risk
Analysis Research Center. The two new centers are the Research and Development Center for Data Assimilation and theSurvey Science Center. The Research and Development Center
for Data Assimilation was formed to further the research of
the Data Assimilation Research Group (of the Prediction andKnowledge Discovery Research Center) and the Random
Number Research Group (of the Research Innovation
Center), replacing both groups. The Survey Science
Center concentrates on specific areas of research thatare close to problem resolution from among those covered by the Survey Research Group of the Departmentof Data Science, and was formed to replace the Social

Research Innovation

Survey Science

Mathematical
Analysis andStatistical
Inference

Data Assimilation

Data
Science

Risk Analysis(Bottom-up)

Basic Research

Statistical
Modeling

Strategic Research ISM( Top-down)
Prediction and Knowledge Discovery

Research Organization ofInformation and Systems
Transdisciplinary ResearchIntegration Center

NOE Establishing Project
The Institute of Statistical Mathematics is part of the Research
Organization of Information and Systems, whose new mediumterm plan directs the Institute to establish Networks Of Excel­
lence (NOE) in statistical mathematics. We have formed or arein the process of forming NOE in five research areas: integrated
risk research, survey science, next-generation simulation sci­ence, statistical machine learning, and service science. The
NOE pursue activities to establish new methodologies in theirrespective research fields and serve as hubs for interdisciplinaryinteraction.
We have established an NOE Forwarding Unit to facilitate
and provide overall coordination for advancing the NOE
Establishing Project. The NOE Forwarding Unit provides
support and coordination that is crucial to the goal of estab­lishing a fourth scientific methodology (i.e., data centricscience) in our knowledge society, beyond efforts to resolve
individual problems. The NOE Establishing Project draws on
advice from experts in the private sector and from universities
and government organizations. Using this advice, the Managing
Committee of the NOE Establishing Project comprehensively
formulates a unified strategy for managing the project.
Acting as central hubs, the Risk Analysis Research Center,
Survey Science Center, and Research and Development Center
for Data Assimilation have already formed NOE in the respec­tive areas of integrated risk research, survey science, andnext-generation simulation science. The NOE Research Promotion Organization centrally oversees and coordinates all
activities by the five planned NOE to advance research ineach respective area from the standpoint of establishing
new methodologies (Figure 2). The Institute will establish a
Research Center for Statistical Machine Learning and aService Science Research Center later in 2011 fiscal year.
The establishment of these centers will create a framework
to enable the inter­disciplinary transfer of knowledge revolving around the Institute of Statistical Mathematics.
For more information about the NOE Establishing Project,please visit http://noe.ism.ac.jp/english/.

Research Centers

The Institute of Statistical Mathematics pursues research andeducation along the two lines of basic research and strategic
research. As shown in Figure 1, these are performed respectively
by basic research departments, arranged horizontally, and thestrategic research centers, arranged vertically. By its nature,statistical mathematics cuts across various broad disciplines,
linking them. The basic research departments develop theinterdisciplinary tools that make this possible.
The phrase “basic research” is used rather than “fundamental”
or “foundational research” because this best describes the two
major qualities of statistical mathematics. To echo the wordsof our former Director-General, Genshiro Kitagawa, the field ofstatistical mathematics must itself evolve to meet the changing
needs of society and our data environment, and is therefore afield of study that is neither fixed nor universal in nature. At thesame time, there are approaches and directions at the heartof statistical mathematics that remain unchanged as the fieldevolves.
There are three basic research departments: statistical mod­eling, data science, and mathematical analysis and statisticalinference. These departments engage in cutting-edge research
to develop methodologies for rational prediction and decisionmaking based on data and existing knowledge. All of theInstitute’s permanent researchers are assigned to one of thebasic research departments.

Survey Information Research Group (of the Research Innovation Center). In addition to the four strategic research centers,
the Institute operates the Research Innovation Center as an
incubation center for pursuing innovative research that will
facilitate the growth of the two new strategic research centers.

Figure 1. Basic Research Departments and Strategic ResearchCenters of the Institute of Statistical Mathematics

Figure 2. Institute of Statistical Mathematics and Networks Of Excellence5Research Centers
Prediction and Knowledge Discovery Research Center

The Role of Probability Forecast of Earthquakes
and the Standard Statistical Model

Research Centers

The occurrences of earthquakes are not com­pletely disordered but obey a lot of statisticalempirical laws, and a probabilistic forecast is pos­sible to some degree. For instance, the epidemictype aftershock sequence ( ETAS) model has
been developed in the Institute of Statistical
Mathematics, and is a standard stochastic modelthat forecasts the earthquake occurrence rateby using the data of past earthquakes. It is usedworldwide for the comparison research between
simulated and real seismicity data. The earthquake
forecasting by the ETAS model will be adopted in
U.S. California state. We further study anomalies
in seismic activity by diagnosis analysis using the
(1)

(2)

ETAS model. This model evolves to accept serious
needs under huge hypocenter data so that various
effective analyses are possible.
The research of earthquake mechanisms has
advanced with steady steps. However, it is very hard
to find a silver bullet for earthquake prediction.
Even if a promising finding comes out, it doesn’t
fully contribute to disaster prevention unless it
accompanies some quantification and modelingthat lead to prediction. The stochastic forecastingis realistic if we think about a complexity andregional diversity of the earth’s crust internalfaults and the stress distribution. To place thisin the starting point and steadily advance it, likepiling up the bricks, is an intention of theinternational Collaborative Study of Earth­quake Predictability (CSEP) project. The
CSEP project will advance the revision of thestandard seismic activity models which suitvarious places in the world. Also, a variety ofearthquake forecasts can be evaluated by thecomparisons. They are objectively measured
and can be compared by statistical methods.
As a matter of course, inclusive researchesof earthquake mechanisms and abnormality
phenomena are indispensable to forecast alarge earthquake with higher probability gains
than the standard statistical model does, andto obtain a better evaluation that shows theadvancement of the forecast. It is necessary
for researchers to digest many examples to
see the imminence and the uncertainty oflarge earthquake generation from the various
anomalous phenomena. Those findings arebuilt in an advanced model to achieve a better
forecast than the standard seismic activity
model.
Yosihiko Ogata

Figures:
Examples of regional earthquake probability forecast:(1) The colors show probabilities per unit area and timethat an earthquake of magnitude (M) five or larger occurs
in the region. The asterisks show large earthquakes ofM6.7 or larger that occurred in the last 15 years.
(2) One year probability forecasts that an inland earthquakeof M4 or larger occurs per unit area.

6

A Long Way for Building Prediction Model to Enhance Personalized Medicine• Machine learning approach
Our original strateg y is to extract precious
information by the adva nced use of machinelearning techniques. Currently a boosting machinefor maximizing Partial Area under the Curve for theprediction of the phenotype is published. In this way
the original strategy gets successful outcomes, but weface to a difficult aspect unsolved for a practical useof prediction model. We can suggest that one of themost difficult problems is a problem of reproducibility
for performance of the prediction model when it isapplied to data sets got from different institutions. For
example, there is a strange phenomenon for behaviors
of test error. Figure expresses a plot presented at a

final stage for building prediction model with n = 59.
We did partition two groups of 49 patients and of
10 patients randomly 100 times. In every partition
we build the best prediction model by 10-fold crossvalidation. We conclude the best prediction scorewith test error 19.1 percent in average, which suggests
a reasonable performance. However, surprisingly
we observed a substantial bias when we monitored
the error rates of all subjects for 100 replications.
There are 26 patients with correct classification in
all 100 trials; 6 patients with all wrong classification.
Thus we encountered there are suggested serious
problems even if the prediction is apparently
appropriately performed. In the long run we could
detect inconsistency of a score factor, and suggested
reanalysis in the co-operative work.

Research Centers• Genome omics data
This project has been started to exploit statisticalmethods for knowledge discovery for extracting
information in the vast hull of genome and omics data
sets since 2004. Here genome and omics data denote
typically SNP (Single Nucleotide Polymorphism) data,
microarray data for gene expression and proteome
data for protein expression. Presently our groupproceeds co-operative work with research teamsof National Cancer Center and those of Mitsubishi
Chemical Holdings in which we challenge statisticalprediction for a phenotype expressing pathologicaldiagnosis or treatment effect on the basis of genome
and omics data sets. For a final goal we aim to build
a prediction model for the phenotype based on a
specific set of gene expressions. There is underlying
a difficult problem called ‘ p << n ’ problem, where p
and n denote the dimension of data vector and sample
size, respectively. Recently, the quality of genome andomics data has been improved. A data base named‘Gene Expression Omnibus’ administrated by National
Center for Biotechnical Information in USA collects
more than 540.000 data sets for gene expression.

• Reproducibility
We attain to understand there are several causes
for the problem of reproducibility with complicatedsituations. We can summarize that almost causes
are attributed to a situation in data are observationalrather than sampled by random design. Therefore,
there comes into existence of selection bias relatedwith heterogeneous variables behind patients andinconsistent treatment protocols and so forth. In
fact, disease type ranges over cancer, stroke andcardiac infarction with severe cases in which thetreatments are apprehensive over the adverse effect.
Hence random clinical design is almost impossible
in the ethical sense. In statistical literature theproblem of selection bias has been highly discussed
in conformal paradigm; it is not well established in
explanatory paradigm. However, if there is a new
objective to employ statistical prediction, then we
have to do interactive thinking between explanatory
and conformal discussion with concurrent manner.
For this we will deeply discuss all possible ideas tostrengthen prediction
performance and theuniversality for differ­
ent data sets.
Shinto Eguchi

Figure:Plot of averaged test errors
for 59 patients7

Risk Analysis Research Center

Evaluating Benefit-Risk Balance of Food and Drug
• Mission
For establishing a proper usage of drug and foodin our society, evidence regarding their benefit
and risk is needed. Actually, the primary evidence
is acquired from clinical/epidemiological studies.
In the Food and Drug Safety Research group, we
have conducted researches on statistical methods
for design and analysis of clinical/epidemiologic
studies on drug and food and their application toreal problems.

Research Centers• Design and analysis of clinical trials

for drug development
Although general theory and methodology fordesign and analysis of clinical trials have been welldeveloped, appropriate statistical methods should
be selected respectively and carefully incorporat­
ing specific characteristics of the disease and drug
under study. In many cases, developmentof novel statistical methods with improv­
ed performance is warranted. We havedeveloped several statistical methods
and demonstrated their performancethrough application to real examples andMonte Carlo simulation.
• Development of statisticalmethodologies for accelerating
predictive medicine
Advances in genomics and biotechnol­
ogy have established molecular heteroge­neity of human diseases and accelerated
clinical researches for predicting patients’susceptibility for certain toxicities anddisease outcomes using genomic infor­
mation to guide treatment decisions forindividual patients. In promotion of pre­dictive medicine, drug development isundergoing a paradigm shift, and new

Figure:Codevelopment of predictive biomarker
and drug: basic designs of randomized
clinical trials with predictive biomarkers

8methods of the design and analysis of clinical trials
for codevelopment of predictive biomarkers andrelevant drugs are needed. We conduct researches
on statistical methods in this field with theirapplications to many clinical genomics studies.
• Benefit/risk analysis of drugs basedon large-scale database
Various large-scale databases have been avail­able for the scientific benefit/risk evaluation andrisk management of post-marketing drugs in Europe
and North America. In Japan, however, such alarge-scale database does not exist. As the first stepto improve this situation, we are building original
databases using data collected from pre-marketingclinical trials and post-marketing surveillance, andwe conduct benefit/risk analysis of drug effect.
Shigeyuki Matsui

Research Centers

The Effort of the Statistical Science towards Solution of Environmental Problems• Ambient water quality criteria

for dissolved oxygen in Japan
Oxygen is one of the most essential environmen­
tal constituents supporting life. Ministry of theEnvironment Government of Japan has derived aset of dissolved oxygen criteria to protect specificaquatic life communities. The use of the specificstatistical methods is recommended for assessing
spatial and temporal water quality criteria exceed­
ance in the Tokyo Bay. (Fig.1)• On persistent organic pollutants
To identify the sources of pollution by persistentorganic pollutants (POPs), this study is developing
statistical methods to make inference on pollution
sources besides organizing POPs data. Further, thisstudy is making contribution to the development oftechnology to reduce the risk of POPs in vegetable
farming. (Photo 1, Fig 2)• Risk-hedge oriented optimization systemagainst natural and managerial disaster towardsustainable forest resource management
After the Kyoto protocol entered into force, reduc­tion of carbon dioxide in the atmosphere is promised
to slow down the degree of global warming. Amongthe proposed mechanisms under the Kyoto protocol,
forest resources are regarded as a source of carbon
sequestration, where forestation, deforestation
and reforestation are the main human-induced
activities. Housing and furniture manufacturing is
also contributing to carbon mitigation indirectly.
Despite of these facts, unfavorable current economic
situation for forestry business discourages forest
owners to abandon forestry practice in mountain­
ous regions. Besides, typhoon disaster and snow
damage on forest stands is accelerating abandoning
phenomena. In our research project, we constructa risk-hedge oriented optimization system towardsustainable forest resource management. In thesystem, we try to predict events of natural disasterand managerial disaster spatially and temporally,
and control themanagement scheme to avoid thesedisasters with a view of these phenomena. (Photo2, Fig 3)
Koji Kanefuji

Photo 1: Growing experiment of cucumbers( Niigata Agricultural Research Institute )Research Centers• Mission
A risk analysis research center/environmental risk
research section aims at performing the contribu­
tion towards solution of the environmental problem
which is a modern subject by developing the optimal
new statistical methodology for each environmental
problem (for example, the problem of dioxin, theproblem of global warming, the problem of con­tinuous use of safe water). Moreover, in order torealize this purpose, in cooperation with the commu­
nity of environmental science, research is carried out
including a visiting teacher or a project researcher.

Fig.2: Relation between concentrations of dieldrin

Fig.1:
Tokyo Bay environmental baseline survey

Photo 2:
Scheme to avoid windstorm in DenmarkFig.3: Experiment for wind tolerance( Provided by Dr. H. Ishikawa, Tokyo Univ. of Sci.)

9Research Innovation Center

Automatic Speech Recognition Using Logistic Regression

Research Centers• Difficulties in discriminative approaches
Although discriminative approaches like the sup­port vector machine or logistic regression have
had great success in many pattern recognition
applications, they have achieved only limited
success in speech recognition. Two of the difficulties
often encountered are 1) speech signals typicallyhave variable lengths and 2) speech recognition isa sequence labeling problem, where each spoken
utterance corresponds to a sequence of words orphones.

be used with kernel logistic regression, as shown in
Figure 1. Unlike previous work that has used HMMs
in combination with a discriminative classification
approach, we jointly optimize the logistic regression
parameters and the HMM parameters using apenalized likelihood criterion.
In experiments on phone classification using the
TIMIT database, the phone accuracy increased from
70.6% to 79.9%. The joint optimization improves therecognition accuracy significantly.
• Two-step approach for handling• New framework using logistic regression
We solve the problem of variable-length speech
signals by including a mapping in the logistic
regression framework that transforms each speech
signal into a fixed-dimensional vector. The mappingis defined either explicitly with a set of hidden
Markov models (HMMs) for use in penalized logistic
regression or implicitly through a sequence kernel tothe sequence labeling problem
A two -step approach is used to handle thesequence labeling problem. In the first step, a set of
HMMs is used to generate an N-best list of sentence
hypotheses for a spoken utterance. In the second
step, these sentence hypotheses are rescoredusing logistic regression on the segments in the
N-best list (Figure 2). A garbage class is introducedin the logistic regression framework to getreliable probability estimates for the segmentsin the N-best lists.
The two-step approach was test­ed on aconnecteddigit recog­ni­tion task. The baseline
conven­t ional approach achieved sentence
accuracy of 96.9%. Rescoring 5-best lists withlogistic regression im­proved the accuracy to
98.3%.
Tomoko Matsui

Figure 1: The kernel logistic regression model

Figure 2:
The 5-best list after rescoring us­
ing penal­ized logistic regression
with HMM likelihoods as regres­sors. The hypotheses have beenreordered according to sentence
probabilities computed from geo­
metric means of the seg­ment prob­
abilities.  Sil means silence.

Joint work with Oystein Birkenes (TANDBERG), Kunio Tanabe (Waseda U.),
Sabato Marco Siniscalchi (U. of Palermo), Tor Andre Myrvoll (SINTEF), and Magne Hallstein Johnsen (NTNU)

10

Research Centers

Development of Inference Techniques Based on Optimization Methodology

Figure 1: CS clinch numbers ( Courtesy of Kyodo News )50• Optimization of measure and information theory
Our group is also developing the theory andalgorithms for estimation of distributions basedon optimization in the space of measures. The
problem of communication channel capacity in
information theory is an example of nonlinear
convex optimization in the space of measures.
The capacity of a noisy communication channel
is the tightest upper bound on the amount of
information that can be transmitted in a unit timeover the channel, and is given by maximizing themutual information defined with the probability
measure of an input signal and the communication
channel itself with respect to the measure undersome constraints. It is extremely interesting thatthe capacity-achieving probability measure has
shown to be discrete in many cases as in Fig. 3. We
are trying a semi-infinite optimization approach inorder to account for the discreteness of the optimalmeasure and its extension to vector channels.

[msec]

Satoshi Ito

Figure 2: Distributed samples ( Courtesy of Kyodo News )

1
0.5

p( θ)

0
0.75

1

1.5

2

κ

2.5

3

3.5

4

4.5

5

10

20

κθ

• Discrete optimization and service sciences
Discrete or combinatorial optimization is becom­
ing increasingly important in our present society.
For instance, optimal arrangement of humanresources is a typical and common issue in service
sciences. Thinking of the future contribution of
statistical mathematics in various fields of service
sciences, we must advance the study of discrete
optimization as an important discipline. As an
example of such problem solving, we developed
computational algorithms for clinch and elimination
numbers in league sports. The clinch (elimination)
number is the minimal number of future wins (losses)
needed to clinch (to be eliminated from) a
specified place or higher in a league. The
algorithms were applied to play-off spotsof Japanese professional baseball leagues,
and the Kyodo News started distributing
the result numbers named the CS clinch
numbers to its member companies during
the 2010 season (Figs. 1 and 2).

Research Centers• Outline of our research
Computational inference is essential for under­
standing various phenomena from given data, andmathematical methodologies for computational
inference are always asked to be adaptable tochangeable data both in quality and in quantity.
Our research focuses on optimization methodology
as a fundamental tool for computational inference
and aims to develop new inference techniques instatistical applications. We are studying several
topics including the estimation of large-scale graphi­
cal models and the 3-dimensional structure ofproteins as well as the following two.

Figure 3: Discrete distributions achieving the capacity ofa single spiking neuron ( Ikeda and Manton, 2009 )11Research and Development Center for Data Assimilation

From Simulation to Data Assimilation ( DA )Research Centers• DA solves your troubles
Even if it took you a lot of hard work to program
a numerical simulation, you may still fail to obtain
results that reproduce the reality as expected. Con­sidering that the time consumed is not negligible
and that the basic idea of the simulation modelis reliable, it is only natural that you would like tomanage to solve the problem. But what should bedone concretely? Data assimilation (DA) gives you a
key to the solution. After calm consideration you willfind that the unexpected results are probably due
to insufficiency of the simulation model. The usual
suspects might be initial and/or boundary conditions
you attached ad hoc, neglected phenomena inthe model, a finite difference approximationof a derivative, empirically used relations andparameters. DA is a process for modifying asimulation model with reference to data and aims
to precisely reproduce and predict a targeted
phenomenon. Methodology and applications of DA

have been used in studies of numerical weather fore­
casting and ocean state monitoring, and DA is being
applied in various other fields.
• DA is time-series analysis
DA allows for two types of uncertainties. One isfor the deficiency of the simulation model, and theother is for the disagreement between the model andobservations. Due to the uncertainties, whereas theoriginal simulation model brings a single solution,
DA provides multiple candidates of the solutions andtheir probabilities. If we represent the uncertainties
by system noise and observation noise, DA can beformulated with a state space model, which is wellknown in the field of time-series analysis. For thestate space model constructed from the simulation
model, we obtain probability distribution of the statevariables, namely the simulation variables, on thebasis of data.

Figure 1:
A data assimilation result for sea
surface height in the equatorial
Pacific:(a) simulation(b) altimetry observation(c) data assimilation• Practice of DA and supercomputers
We are now ready to start DA. We’ll soon be
faced with a computational burden. If the original
simulation model is well constructed, DA withthe model requires substantial computational
resources. The Institute of Statistical Mathematics
owns supercomputers which boast the foremost
specifications for the use of statistical sciences. We
are carrying out large-scale computations of DA anddeveloping algorithms for parallel computing.
Figure 2: A supercomputer at the Institute of Statistical Mathematics

12

Genta Ueno

Research Centers

Data Assimilation Technology Pioneers New Scientific Methods
based on Biomodeling and Simulation• Rapid advance of biotechnology• Whole gene transcription simulationof human lung cancer systems
The invention of DNA microarray chip has ena­
bled us to measure transcript levels of more than
20,000 genes in human genome, simultaneously. Our
collaborators (Professor Miyano and his researchteam at Institute of Medical Science, the Universityof Tokyo) successfully monitored temporal change of
all genes in two phenotypes of human lung cancers
under the treatment with an anticancer agent; onedemonstrating exquisite sensitivity to the drug
treatment and the other being resistant (Figure 1).
Recently, several studies have advocated relatively

rapid acquisition of resistance within a few years
after initiation of the anticancer drug treatment. The
experimental data obtained now will certainly bevital to uncovering molecular basis of maintaining
the viability of drug resistant cancer population. For
the first time in the world, RDCDA succeeded in thedevelopment of whole gene transcription simulatorsthat are highly reproducible to the observed gene
expressions of the drug sensitive and resistantcancer cells (Figure 1). The developed simulation
models will be utilized in the discovery of key
molecules that are promising for pharmacologicaltreatment to improve drug efficacy, and a principleof action in maintaining the viability of the acquired
drug resistance.

Research Centersand data assimilation
Cutting-edge biotechnology has resulted in everincreasing amounts of information disseminating
within our society. The advent of high speed DNA
sequencers is now bringing into reality a new eraof personal genome and personalized medicine.
Within the next several years, this technologicalbreakthrough could cause significant changes inentire research fields of life science, involving every-omics studies, in terms of both quantity and quality.
To uncover a complex world of cellular systems fromsuch a vast amount of information, Research andDevelopment Center for Data Assimilation (RDCDA)
aims to create a new research infrastructure oflife science data assimilation systems involving
experimental bioscience, bio-modeling, simulation,and state-of-art statistical science.

• Biomodeling and HPC
High-throughput generations of large-scale sim­
ulators so as to reproduce observed data on 20,000
endogenous variables would be a quite hard chal­lenge, computationally and statistically, to be ad­dressed with state-of-the art supercomputer systems.
Some researchers in RDCDA are now developing a
new life science data assimilation system as members
involved in a national project on the Next Generationof Supercomputer. RDCDA also has many experts
on high performance computing (HPC), who adviseon the actual implementation of data assimilation
codes using massively parallel supercomputers andon the developments of new parallel computing
technologies.
Ryo Yoshida

Figure 1: Gene expression profiling of drug sensitive and resistant lung cancer cells, and whole gene transcription simulations

13

Survey Science Center

Research on Survey Methodology and Planningof Cooperative Training Survey• Process of social survey and research

Research Centerson survey methodology
The Institute of Statistical Mathematics (ISM) has
been conducting many social surveys for almost
sixty years. One purpose of these surveys is to
study public opinions in Japan. Another important
purpose is to study survey methodology in real-life
settings.
Social surveys mainly consist of two processes, as
is depicted in figure 1. One is the process of sampling(right hand side), where representativeness of thesample is the main concern. The other is the process
of measurement (left hand side), where we try tocapture the opinion of respondents exactly.Since
ensuring good quality control on each step in figure
1 is crucial for valid survey operation, each step has
its own methodological subjects to be studied.
• Practice of survey research by ISM
Formerly ISM’s surveys were administered byourselves with the support from many universities,
employing students as part-time interviewers. But
during the past two decades this tradition has been
lost, entrusting most of the survey operation tosurvey company. Worsening survey circumstancesin the present society do not allow us to ask nonprofessional interviewers to complete our survey.

Figure 1: Process of social survey and subject of researchon survey methodology

14

But this also meant that we have gradually lost
opportunities for research on survey methodology.
• Planning of the collaborative training survey
More recently, social science departments of many
universities have undergone curriculum reforma­tion which places more emphasis on survey practice.
In line with this trend, Survey Science Center is goingto administer what we call “Collaborative Training
Survey”, under close cooperation with university
departments and other survey-related institutions.
Thus, we will offer faculty members and students
more chances to participate in large-scale surveys.
As a starting point, we conducted a nationwide
survey on “Social Stratification and Social Psychol­ogy” (SSP survey) in 2010 with Osaka University.
We operated sampling or interviewing process of
30 survey locations by ourselves (figure 2). Morethan twenty graduate and undergraduate students
participated as interviewers.
We are planning to continue this kind of survey
practice for the coming years, with the supportof visiting professors from outside the institute.
The goal of this activity is to broaden the base of
research on survey methodology by fostering moresurvey professionals.
Tadahiko Maeda

Figure 2: 250 locations of SSP 2010 Survey:
Blue dots indicate 30 locations where samplingor interviewing were operated by ISM

Research Centers

Building a Database of Social Survey Results• Objective of the social survey information• A comprehensive report of a survey

on the Japanese national character
The core data used to develop the database is
obtained from a survey on the Japanese national
character, which is conducted on a regular basis by
the Institute of Statistical Mathematics. This sur­vey has been conducted every five years since thefirst 1953 survey. The most recent was its twelfth

survey and was conducted in the autumn of 2008.
The questionnaire items in the survey concerned
the attitudes of the participants toward life, environ­ment, religion, politics, race, the Japanese people,
and so forth. The results of this longitudinal survey,
which are crucial for evincing the transition ofJapanese character, are already available on theInternet. More comprehensive results, including
cross-tabulation tables and graphs, are also provided
on the Internet.

Research Centers

research project
The Social Survey Information Research Project at
the Survey Science Center has been collecting social
survey results with the aim of building a databaseof social surveys. A single survey provides limited
information, particularly in the recent deteriorated
survey environment. Multidimensional analysis
using various types of social surveys is essential forcomprehending the complicated aspects of modern
society. This research project studies the method­
ologies for building a database and develops statis­
tical methods for analyzing data. The group also
intends to make the database available for public use.

• Database of related surveys
The survey on the Japanese national characteris by no means the only survey conducted by
the Institute. Various interrelated comparative
surveys and experimental surveys have also been
conducted. One of them is public opinion survey inTokyo conducted since 1953 to 1982. The results ofthese surveys are also included in the database andmade accessible to the public one at a time. The
potential users of this database, including not only
researchers but also the general public, will be able
to analyze the data from various perspectives.

Takahiro Tsuchiya

Figure: Browsing survey results on the web

15

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washington)

July 27, 1988 -Stichting Mathematisch Centrum

The Kingdom of the Netherlands (Amsterdam)

May 10, 1989 -Statistical Research Center for Complex Systems, Seoul National University

The Republic of Korea (Seoul)

October 17, 2002 -Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany (Berlin)

December 8, 2004 -Institute of Statistical Science, Academia Sinica

Taiwan ( Taipei)

June 30, 2005 -The Steklov Mathematical InstituteRussia (Moscow)

August 9, 2005 -Central South University

China (Changsha)

November 18, 2005 -Soongsil University

The Republic of Korea (Seoul)

April 27, 2006 -Department of Statistics, University of Warwick

The United Kingdom (Coventry)

January 16, 2007 -The Indian Statistical Institute

India (Kolkata)

October 11, 2007 -Department of Empirical Inference, Max Planck Institute for Biological Cybernetics

Germany (Tubingen )August 11, 2010 -Research Collaboration
This academic study program provides researchers from other academic institutes with access to thefacilities of the Institute, and provides opportunities for researchers to conduct theoretical and appliedstudies on statistics.

Research Cooperation■ Number of Activities
2005

2006

2007

2008

2009

2010

124

122

120

138

154

135

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below tofind the most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Spatial and time series modelingfComputational statisticsbIntelligent information processinggMathematical statisticsc

Graph modelinghLearning and inferencedSurvey researchiComputational mathematicseMultidimensional data analysisj

OthersMajor Research Fields
Number16

FieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, law, politics, society, management, official statistics, population, etc.

8

OthersOther research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education
and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

President

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Graduate School Program

Outline of Education and Research
The course includes designing of data-gathering systems, modeling, inference and forecasting in order
to extract information and knowledge from the real world based on empirical data, as well as basic,mathematical and applied education and research related to these fields. The course aims to provide thestudent with skills that help to contribute to solving important and connected issues and give the ability
to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

17

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science byprofessors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of

the best in the world.

● The library holds an extensive collection of books and journals, being one of the best in the world.
● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible

for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted● Requirements to complete the course are as follows:

Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research

results.

Graduate School Program

Number of Students ( As of April 1, 2011)■ Doctor’s course three years■ Doctor’s course five years

Year of enrollment

2004

2007

2008

2009

2010

2011

Year of enrollment

2007

2008

2009

2010

Number of students

2

2

4

5

5

5

Number of students12

1 (1 )

2* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University (1)  • Tohoku University (2)  • Fukushima University (1)  • University of Tsukuba (6)  • Saitama University (1)  
• Ochanomizu University (1)  • Hitotsubashi University (6)  • Chiba University (1)  • The University of Tokyo (16)  • Tokyo Gakugei
University (1)  • Tokyo University of Agriculture and Technology (1)  • Tokyo Institute of Technology (4)  • Tokyo University of Marine
Science and Technology (1)  • Shizuoka University (1)  • Nagoya University (2)  • Toyohashi University of Technology (2)  • Kyoto
University (4)  • Osaka University (2)  • Okayama University (2)  • Shimane University (3)  • Kyushu University (2)  • Oita University (1)  
• Japan Advanced Institute of Science and Technology, Hokuriku (1)  • Osaka City University (1)18Graduate School Program

University Background of Students
Private universities• Keio University (5)  • Chuo University (7)  • Tokyo University of Science (5)  • Toyo University (1)  • Nihon University (2)  
• Japan Women’s University (1)  • Hosei University (7)  • Waseda University (5)  • Nanzan University (1)  • Kyoto Sangyo University (1)     • Okayama University of Science (1)  • Kurume University (1)  
Foreign universities• Aston University (1)  • Center for Analysis and Prediction, China Seismological Bureau (1)  • Chinese Academy of Sciences,
Institute of Applied Mathematics (1)  • Jahangirnagar University (2)  • Northeastern University, China (1)  • Ohio University (1)  
• Stanford University (1)  • The Hong Kong University of Science and Technology (1)  • Universidade Eatadual of Campians (1)   
• University of Colorado at Boulder (2)  • University of Dhaka (2)  • University of Hawaii (1)  • University of Malaya (1)  • University ofRahshahi (2)  • University of Science and Technology of China (1)  

Degrees AwardedYear

Doctor of Philosophy

Year

Doctor of Philosophy

Year

Doctor of Philosophy

1996
1997
1998
1999
2000

3 [ 1]
1
4 [ 1]

2001
2002
2003
2004
2005

5
4
8 [ 1]
4
4

2006
2007
2008
2009
2010

8 [ 1]
7 [ 1]
4 [ 1]
5 [ 1]
7 [ 1]

6
5* [ ] Ph.D. on the basis of the dissertation only (included in the total)

Current Position of Alumni ( As of April 1, 2011)

Graduate School Program

National and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine, Professor (1)  • University of Tsukuba, Professor (2)  • University of Hyogo,
Professor (1)  • Saitama University, Associate Professor (1)  • The University of Electro-Communications, Associate Professor (1)  
• The University of Tokyo, Associate Professor (1)  • Kyushu University, Associate Professor (1)  • Kyushu Institute of Technology,
Associate Professor (1)  • The Institute of Statistical Mathematics, Associate Professor (1)  • University of Tsukuba, Lecturer (1)   
• Hokkaido University, Assistant (1)  • Chiba University, Assistant  • Tokyo Institute of Technology, Assistant (1)  • Hiroshima University,
Assistant (1)  • Kyushu University, Assistant Professor (1)  • University of the Ryukyus, Assistant (1)  • The Institute of Statistical
Mathematics, Assistant Professor (5)  • The University of Tokyo, Project Researcher (1)  • Kyoto University, Project Researcher (1)   
• Tokyo Institute of Technology, Research Fellow (1)  • Nara Institute of Science and Technology, Research Fellow (1)  • The Institute of
Statistical Mathematics, Project Researcher (10 )  • Bank of Japan, Project Post (1)  • Japan Broadcasting Corporation (1)  • Financial
Services Agency, Financial Research and Training Center, Researcher (1)  • The Institute of Statistical Mathematics, JST CREST
Research Fellow (1)  • JST Basic Research Programs Doctoral Research Fellow (1)  • Railway Technical Research Institute, SeniorResearcher (1)  • Statistical Information Institute for Consulting and Analysis (1)  • Government Pension Investment Fund (1)  • Public
School, Teacher (1)  • The Institute of Statistical Mathematics, Research Fellow (2)  
Private universities• Sapporo Gakuin University, Professor (1)  • Meiji University, Professor (1)  • Doshisha University, Professor (1)  •Tokyo Health Care
University, Associate Professor (1)  • Nihon University, Associate Professor (1)  • Tokyo University of Information Science, Lecturer (1)  
• Josai University, Lecturer (1)  • Sapporo Gakuin University, Full-Time Lecturer (1)  • Tokyo Women’s Medical University, Postdoctoral
Fellow (1)   • Shibaura Institute of Technology, Lecturer (1)Foreign universities• Asia-Pacific Center for Security Studies Department, Associate Professor (1)  • Central South University, Professor (1)  • Hong
Kong Baptist University, Lecturer (1)  • Jahangirnagar University, Professor (1)  • Jahangirnagar University, Associate Professor (2)   
• Massey University, Research Fellow (1)  • The University of Warwick, Research Fellow (1)  • University of Otaga, Research Fellow (1)  
• University of Rajshahi, Associate Professor (1)  • University of South Carolina, Research Fellow (1)  • Victoria University, Senior Lecturer (1)Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory, Research Fellow (1)  • NTT Communication Science Laboratories, Research Fellow(1)  • Seiwa Kikaku (1)  • Toyota Motor Corporation, Higashi-Fuji Technical Center, Research Fellow (1)  • NLI Research Institute,
Chief Researcher (1)  • Sankyo Co., LTD. (1)  • Mizuho Trust and Banking, Senior Researcher (2)  • JP Morgan Trust Bank Limited,
Vice President (Hosei University, Part-Time Lecturer) (1)  • ATR Computational Neuroscience Laboratories, Research Fellow (1)  
• Schlumberger Limited (1)  • Macquarie Securities, Japan, Quantitative Analyst (1)  • Non-Life Insurance Rating Organization of Japan,
Staff Member (1)  • Open Technologies Corporation (1)  • Yamaha Corporation (1)  • CLC bio Japan,Inc (1)  • Pfizer Japan Ink (1)  
• Bank of Tokyo-Mitsubishi UFJ (1)  • Goldman Sachs Asset Management L.P. (1)19Outreach Activities

Tutorial Courses■ History
The statistical education program started in 1944, the year that the Institute of Statistical Mathematics
was founded, as an education program at the Numerical Computation Training Center of the Science
Research Technical Assistant Training Center of the Ministry of Education, located in the Institute. In
1947, the affiliated Statistical Technician Training Center was opened as an educational organization for
statistical technicians and instructors, in order to improve the levels of staff training within the statisticalorganizations of the government and to supply statisticians.
As social needs have changed, the purpose of the education program has gradually shifted away fromthe initial aim of supplying well qualified statistical technicians for the government, towards statisticaleducation for working people. Tutorial courses were therefore initiated. Later, statistical methods became
more widely used across a broad range of fields. Consequently, more extensive and sophisticated statisticaleducation was required. To meet this demand, tutorial courses were expanded to cover a wider range ofstatistical topics. From 1965 to 1985, six to eight courses were held annually, not only in Tokyo, but also inprovincial cities such as Osaka, Okayama and Fukuoka.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation,and the affiliated Statistical Technician Training Center was abolished. However, in response to consistentpublic demand for tutorial courses, the Center for Engineering and Technical Support, together with otherdepartments, ran three to four courses annually. In 2005, the number of courses rose to 13. In 2010, thenumber of courses was 13.

■ Courses
The total number of courses held from 1969 to March, 2011 was 272, with a total of 20,006 participants.
These courses covered a wide range of fields from basic to applied statistics. The following table lists thecourses held in the past 3 years:

Outreach Activities

Year

Category

Title

Month        Number of participants

2008

Basic course

Statistical Causal Analysis by Structural Equation Modelling

February

58

Standard course

Regression Models for Count Data and Their Extension

February

63

Basic course

Data Analysis and Simulation with R

May

69

Basic course

The Grammar of Science and Descriptive Statistical Methods

June

47

Basic course

Introduction to Statistics

July

79

Standard course

Spatial Point Pattern Analysis — Introduction from Biological Examples

August46

Standard course

Statistics of Extremes

August48

Standard course

An Introduction to Statistical Analysis by the Theory of Martingales

September48

Basic course

Analysis of Sample Surveys with R

September

41

Basic course

Introduction to Multivariate Analysis

October

71

Standard course

Data Assimilation: State Space Model and Simulation

October72

Basic course

Quality Control, Quality Engineering (Taguchi Method)

November17

Basic course

Introduction to Pharmacoepidemiology

November

71Standard course

Understanding of Evolutionary Computation: Statistical Viewpoint

December

56

Standard course

Markov Chain Monte Carlo : Basics and Examples

February

78

Standard course

Data Assimilation Methodology in Practice

April

77

Basic course

Introduction to Sampling Methods and Survey Data Analysis

May

71

Standard course

Introduction to Multivariate Descriptive Data Analysis by R

May

47

Basic course

Statistical Data Visualization

June

75

Basic course

Introduction to Pharmacoepidemiology

June

73

Basic course

Data Analysis for Marketing

June-July

74

2009

20

Tutorial Courses
Year

2010

2011

Category

Title

Month        Number of participants

Basic course

Akaike Information Criterion and Statistical Modeling:
Introduction from Field Biological Data

July

73

Basic course

Introduction to Statistics

July

78

Standard course

Regularization and Estimation— LASSO and Related Topics, Basic Theory and Applications —August44

Basic-Standard course

Elementary of Multivariate Analysis

August76

Basic course

Statistics Literacy for the Law Court— For the Base on the Rational Discussion —November36

Basic course

Introduction to Text Mining with R

November

51

Standard course

Markov Chain Monte Carlo : Basics and Examples / 2010

February

97

Basic course

Introductory Time Series Analysis with R

April

66

Standard course

Robust Inference

June

93

Basic course

Analysis of Sample Surveys with R

June

38

Basic course

Introduction to Multivariate Analysis

August97

Standard course

Statistics of Extremes

August40

Basic course

Statistical Mathematics for Diversity:
Current Topics in Biodiversity of Ecological Communities

August37

Basic course

Introduction to Statistical Quality Control

November

44Standard course

Mathematical Optimization and Its Application

November50

Basic course

Bayesian Designs in Clinical Trials: Principles and Applications

December

98Standard course

Statistical Data Analysis with Positive Definite Kernels— Basics and Advances of Kernel Method

January64

Standard course

Introduction to High Performance Computing by Using R

January

52

Basic course

Introduction to Statistics

February

93Standard course

Markov Chain Monte Carlo : Basics and Examples/2011

February96

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html

Statistical Mathematics Seminar Series

Outreach Activities

Starting in 2011, the Institute has changed the format for our weekly seminar series on statisticalmathematics, which is held every Wednesday. The seminars are led by our academic staff or by outside
researchers from Japan and abroad. Previously, the seminars began at 4:00 p.m. and ran for one hour. The
new format now features two separate lectures, eachlasting 40 minutes. These lectures are free to attend.
To view the seminar schedule and learn more about
the program, please visit the Institute of Statistical
Mathematics website.
http://www.ism.ac.jp/21Outreach Activities

Open Lecture
We hold an open lecture during Education and Culture Week every year (November 1 through 7), tointroduce the Institute’s activities and to promote statistical science. We invite lecturers to speak on atimely topic relating to statistical science. The lecture is open to the general public. For further information,please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/

Consultation about Statistical Science
The Institute provides a consultation service on statistical science for the general public and researchers
as a means of actively sharing the benefits of our research with society. This service operates throughout
the year. Please contact the Center for Engineering and Technical Support. The consultation service covers a variety of topic ranging from the basics of statistics to more specialized issues. Half of the advice
receivers are from the private sector, and the rest are staff from public organizations, university teachers,
and students. Each teacher at the Institute directly takes charge of about 20 specialized cases annually,some of which are publicized at academic conferences, as being of benefit to society.

Annual Research Meeting
The Annual Research Meeting is a tradition that has continued since the Institute was established in1944 as a way for researchers to orally present their research results. Starting in the 2011 academic year,
the annual event changed format to become an open house program held every summer, with professors
and project researchers of the Institute exhibiting poster presentations that describe their research
results from the past year. The Institute piloted the new format in 2010 by organizing an event featuring
poster presentations from all of the researchers of
the Institute. The event was held on July 14, 2011 in
the spacious entrance foyer and featured posters
from 77 researchers in total, covering diverse sub­jects ranging from basic research to the impact ofstatistical science on modern problems. Twenty-two
postgraduate students in statistical science fromthe Graduate University for Advanced Studies also
participated and contributed posters. For a list of
the poster presentations in 2011, please visit ourOpen House website.
http://www.ism.ac.jp/openhouse/

Outreach Activities
22Facilities and Equipment
Facilities and Equipment

Computation Resources
Since January 2010, the Supercomputer System for Statistical Science has been in operation and has
analyzed a large volume of statistical data. The main components are a shared memory system (Fujitsu
SPARC Enterprise M9000 with 2 nodes, the first with 64 quad-core SPARC64 CPUs and 2 TB of main
memory, and the second with 24 quad-core SPARC64 CPUs and 1 TB of main memory), and a distributed
memory system (Fujitsu PRIMERGY RX200S5 with 360 nodes, each with 2 quad-core Xeon X5570 CPUs
and 48 or 24 GB of main memory. In total, 2880 cores and 12.1 TB of memory are available). The system
also includes a large-scale shared storage system (1.37 PB disk storage supported by RAID6), a physicalrandom number generating system (two random number generator boards, each of which can generaterandom numbers at 400 MB/s) and a visualization system (including a SXRD projector with a maximum
resolution of 4,096 × 2,160 and a 200-inch rear projection screen).
In the office building, the primary Local Area Network (LAN) consists of an Ethernet network using
10GBASE-SR for the main trunk and 1000Base-T for branches. The personal computers in researchers’
offices, the Supercomputer System for Statistical
Science are all connected to this network. A wireless
LAN system that supports IEEE 802.11a,b,g,n is alsoavailable in the area of the building occupied by theinstitute. These LAN systems enable distributed
processing and computing resources and statistical
data to be used effectively. Comprehensive network
security measures have been implemented such asa firewall system, anti-virus software, and an intrusion
prevention system. To encourage joint researchwith researchers both in Japan and abroad, as wellas the exchange of e-mails, the network is connectedto the Internet through SINET3 (1 Gbps).
Supercomputer System for Statistical Science

Library and Materials ( As of April 1, 2011)
We have a large number of major Japanese/foreign journals covering a wide variety of fields including
statistics, mathematics, computer science and informatics. In addition, we also have a large libraryconsisting of books on humanities, social science, biology, medical science, science and engineering.
Besides contributed to Japanese and foreign publications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Statistical Mathematics (English; Springer), Proceedings of theInstitute of Statistical Mathematics (Japanese), ISM Survey Research Report (Statistical Researches mainly
related to the Japanese National Character), Computer Science Monographs, Cooperative Research Reports(for collaborative research projects), Research Memorandum, ISM Reports on Statistical Computing, andISM Report on Research and Education.
All materials are properly catalogued
JapaneseJapanese Foreign
and can be searched from the web in
books
periodicals periodicals
order to meet the needs of research­
15,802ers working in a wide of fields. We also
27%
997
1,162
Books
Periodicals 54%
accept photocopy requests.
46%
2,159
58,544Foreign books
42,74273%

23

Finance and Buildings

Administration Subsidy and Others ( 2010 )Type

Personnel expenses

Non-personnel expenses

Total

768,946852,545

1,621,491

Expenditure

Finance and Buildings

Unit: ¥1,000

Accepted External Funds ( 2010)
Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

12

1

4

17

Income121,598

1,100

4,600

127,298
Unit: ¥1,000

Grant-in-Aid for Scientific Research ( 2010 )Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research ( S )1

34,060Grant-in-Aid for Scientific Research ( A)

6

56,160Grant-in-Aid for Scientific Research ( B)3

11,310Grant-in-Aid for Scientific Research ( C)16

17,160Grant-in-Aid for Challenging Exploratory Research

1

600

Grant-in-Aid for Young Scientists ( B )

8

8,840

Grant-in-Aid for Research Activity Start-up

1

1,118

Grant-in-Aid for JSPS Fellows

1

600

37

129,848

1

3,000

Total
Grant-in-Aid for Scientific Research, Ministry of Health, Labour and Welfare

Unit: ¥1,000

Site and Buildings ( As of April 1, 2011)24

Site Area

62,450m 2

Area for Buildings (total)

16,026m 2

Organization

Organization Diagram ( As of April 1, 2011)■ Spatial and Time Series Modeling Group

Cooperative
Research
Committee

Department of Statistical Modeling■ Graph Modeling Group■ Survey Research Group

Department of Data Science■ Multidimensional Data Analysis Group■ Computational Statistics GroupCouncil

Scientific
Advisory
Board■ Intelligent Information Processing Group

Department of Mathematical
Analysis and Statistical Inference
Prediction and Knowledge Discovery
Research Center (PKDRC)■ Mathematical Statistics Group■ Learning and Inference Group■ Computational Mathematics Group■ Molecular Evolution Research Group■ Statistical Seismology Research Group■ Statistical Genome Diversity Research Group

DirectorGeneral■ Environmental Risk Research Group■ Financial Risk and Insurance Research Group■ Research Group for Reliability andQuality Assurance of Service and ProductResearch Innovation Center
Vice DirectorGeneral

Organization■ Food and Drug Safety Research Group

Risk Analysis Research Center( RARC )Research and Development
Center for Data Assimilation■ Functional Analytic Inference Research Group■ Advanced Monte Carlo Algorithm Research Group■ Speech and Music Information Research Group■ Optimization-based Inference Research Group

Survey Science Center
Center for Engineering andTechnical Support■ Computing Facilities Unit
■ Information Resources Unit
■ Media Development Unit

Library

Planning Unit
Evaluation Unit
Administration Planningand Coordination Section

Information and Public Relations UnitIntellectual Property Unit
NOE Forwarding Unit

National Institute ofPolar Research

Planning Section ( NIPR )NIPR / ISM Joint Administration
OfficePlanning Section ( ISM )The Institute of
Statistical Mathematics

General Service Center25

Number of Staff ( As of April 1, 2011)
DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling

4

10

3

17

Department of Data Science

9

4

3

16

Department of Mathematical Analysis and Statistical Inference

5

5

5

15

Center for Engineering and Technical Support

99Administration Planning and Coordination Section

1

1

NIPR/ISM Joint Administration Office

12 ( 26)        1 ( 2)

13 ( 28)13  ( 26)      10 ( 2)72  ( 28)

Total

1

18

19
(

11

) Total Number of Staff of NIPR/ISM Joint Administration Office

Organization

Staff ( As of April 1, 2011)
Director-General

Tomoyuki HIGUCHI

Vice Director-General

Hiroe TSUBAKI

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Hiroshi MARUYAMA

Department of Statistical Modeling
Director Tomoko MATSUI• Spatial and Time Series Modeling Group —————————————————————————————————————————————————————Prof.

Yosihiko OGATA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Ryo YOSHIDA

Prof.

Tomoyuki HIGUCHI

Assoc. Prof.

Genta UENO

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Yoshinori KAWASAKI

Assoc. Prof.

Fumikazu MIWAKEICHI

Assist. Prof.

Shinya NAKANO• Intelligent Information Processing Group ——————————————————————————————————————————————————————Prof.

Hiroshi MARUYAMA

Assoc. Prof.

Yukito IBA

Assist. Prof.

Hiroshi SOMEYAProf.

Tomoko MATSUIAssoc. Prof.

Yumi TAKIZAWA

Visiting Prof.

Koji TSUDA

Prof.

Kenji FUKUMIZU

Assoc. Prof.

Daichi MOCHIHASHI• Graph Modeling Group —————————————————————————————————————————————————————————————————
Assoc. Prof.

Jun ADACHIAssist. Prof.

Ying CAO

Department of Data Science
Director Takashi NAKAMURA• Survey Research Group ————————————————————————————————————————————————————————————————

26

Prof.

Takashi NAKAMURA

Assoc. Prof.

Tadahiko MAEDA

Prof.

Ryozo YOSHINO

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Koken OZAKI

Organization

Staff
Department of Data Science• Multidimensional Data Analysis Group ————————————————————————————————————————————————————————
Prof.

Hiroe TSUBAKI

Prof.

Shigeyuki MATSUIAssist. Prof.

Toshihiko KAWAMURA

Prof.

Nobuhisa KASHIWAGIProf.

Satoshi YAMASHITA

Project Researcher

Hei CHAN• Computational Statistics Group ————————————————————————————————————————————————————————————
Prof.

Yoshiyasu TAMURA

Assoc. Prof.

Seisho SATO

Visiting Prof.

Kazunori YAMAGUCHIProf.

Junji NAKANOAssist. Prof.

Nobuo SHIMIZU

Visiting Assoc. Prof. Norikazu IKOMA

Prof.

Koji KANEFUJI

Adjunct Prof.

Yutaka TANAKA

Project Researcher

Assoc. Prof.

Naomasa MARUYAMA

Visiting Prof.

Michiko WATANABE

Hayato TAKAHASHI

Department of Mathematical Analysis and Statistical Inference

Organization

Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Assist. Prof.

Takaaki SHIMURA

Visiting Assoc. Prof. Hisayuki HARA

Assoc. Prof.

Yoichi NISHIYAMA

Assist. Prof.

Kei KOBAYASHI

Project Researcher

Toshihiro ABE

Assoc. Prof.

Shuhei MANO

Assist. Prof.

Shogo KATO

Project Researcher

Shigeki NAKAGOME• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHI

Assoc. Prof.

Hironori FUJISAWA

Assist. Prof.

Tadayoshi FUSHIKI

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Masayuki HENMI

Assist. Prof.

Shinsuke KOYAMA• Computational Mathematics Group ——————————————————————————————————————————————————————————Prof.

Yosihiko MIYASATO

Prof.

Satoshi ITOProf.

Atsushi YOSHIMOTO

Project Researcher Ryo AKAISHI

Prediction and Knowledge Discovery Research Center ( PKDRC )Director Shinto EGUCHI• Molecular Evolution Research Group ———————————————————————————————————————————————————————————
Assoc. Prof.

Jun ADACHIAssist. Prof.

Ying CAO

Adjunct Prof.

Masami HASEGAWA• Statistical Seismology Research Group —————————————————————————————————————————————————————————Prof.

Yosihiko OGATA

Assoc. Prof.

Jiancang ZHUANG

Visiting Prof.

Shinji TODA

• Statistical Genome Diversity Research Group —————————————————————————————————————————————————————
Prof.

Shinto EGUCHI

Assoc. Prof.

Hironori FUJISAWA

Visiting Prof.

Masaaki MATSUURA

Prof.

Satoshi KURIKI

Assist. Prof.

Tadayoshi FUSHIKI

Project Researcher

Osamu KOMORIAssoc. Prof.

Shiro IKEDA

Assist. Prof.

Shogo KATO

27

Risk Analysis Research Center ( RARC )Director              Hiroe TSUBAKI

Coordinator

Koji KANEFUJI

Vice Director/Coordinator

Satoshi YAMASHITA

Coordinator

Yoshinori KAWASAKI

Coordinator

Shigeyuki MATSUI

Coordinator

Toshihiko KAWAMURA• Food and Drug Safety Research Group ——————————————————————————————————————————————————————————
Prof.

Hiroe TSUBAKI

Project Assist. Prof. Takafumi KUBOTA

Prof.

Shigeyuki MATSUIVisiting Prof.

Yoichi KATO

Visiting Assoc. Prof. Satoshi TERAMUKAI
Visiting Assoc. Prof. Makoto TOMITA

Assoc. Prof.

Masayuki HENMI

Visiting Prof.

Manabu IWASAKI

Project Researcher

Assist. Prof.

Takaaki SHIMURA

Visiting Prof.

Tosiya SATO

Kazuhiko SHIBUYA• Environmental Risk Research Group ————————————————————————————————————————————————————————————

Organization

Prof.

Nobuhisa KASHIWAGI

Visiting Prof.

Yoshiro ONO

Visiting Assoc. Prof. Yoshiyuki NINOMIYAProf.

Atsushi YOSHIMOTO

Visiting Prof.

Mihoko MINAMI

Project Researcher

Tomoko IMOTO

Prof.

Koji KANEFUJI

Visiting Assoc. Prof. Toshihiro HORIGUCHI

Project Researcher

Masaki OKUDA

Visiting Prof.

Kunio SHIMIZU

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Prof.

Kazuo YAMAMOTO

Visiting Assoc. Prof. Hiroshi SYONO• Financial Risk and Insurance Research Group —————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Assoc. Prof.

Yoichi NISHIYAMA

Visiting Prof.

Nakahiro YOSHIDA

Assoc. Prof.

Seisho SATO

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Toshio HONDA

Assoc. Prof.

Yoshinori KAWASAKI

Visiting Prof.

Hiroshi TSUDA

Visiting Assoc. Prof. Toshinao YOSHIBA• Research Group for Reliability and Quality Assurance of Service and Product ————————————————————————————————
Prof.

Hiroe TSUBAKI

Visiting Prof.

Kazuo TATEBAYASHI

Visiting Assoc. Prof. Manabu KUROKI

Assist. Prof.

Toshihiko KAWAMURA

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Assoc. Prof. Hideki KATAGIRIVisiting Prof.

Kakuro AMASAKA

Visiting Prof.

Shusaku TSUMOTO

Visiting Assoc. Prof. Yukihiko OKADA

Research Innovation Center
Director Kenji FUKUMIZU• Functional Analytic Inference Research Group —————————————————————————————————————————————————————
Prof.

Kenji FUKUMIZU

Project Researcher

Yu NISHIYAMA

Assist. Prof.

Kei KOBAYASHI

Project Researcher

Yusuke WATANABE• Advanced Monte Carlo Algorithm Research Group ——————————————————————————————————————————————————
Assoc. Prof.

Yukito IBA

Visiting Prof.

Makoto KIKUCHI

Visiting Assoc. Prof. Koji HUKUSHIMA• Speech and Music Information Research Group ————————————————————————————————————————————————————Prof.

Tomoko MATSUIAssist. Prof.

Shinsuke KOYAMA

Visiting Prof.

Masataka GOTO

• Optimization-based Inference Research Group —————————————————————————————————————————————————————

28

Prof.

Satoshi ITO

Visiting Prof.

Atsuko IKEGAMI

Assoc. Prof.

Shiro IKEDA

Visiting Prof.

Takashi TSUCHIYA

Assoc. Prof.

Genta UENO

Visiting Prof.

Tadashi WADAYAMA

Visiting Assoc. Prof. Yuji SHINANOOrganization

Staff
Research and Development Center for Data Assimilation
Director Tomoyuki HIGUCHI

Vice Director Yoshiyasu TAMURA

Tomoyuki HIGUCHI

Assoc. Prof.

Ryo YOSHIDA

Visiting Assoc. Prof. Toru ONODERAProf.

Yoshiyasu TAMURA

Assist. Prof.

Shinya NAKANO

Project Researcher

Masaya SAITO

Prof.

Junji NAKANO

Project Assoc. Prof. Hiromichi NAGAO

Project Researcher

Kenta HONGO

Assoc. Prof.

Seisho SATO

Project Assist. Prof. Christopher Andrew ZAPART

Assoc. Prof.

Genta UENO

Visiting Prof.

Project Researcher

Kiyohisa SHIBAI

Project Researcher

Kosuke NIKAIDO

Project Researcher

Hitoshi MOTOYAMAProf.

Yoichi MOTOMURA

Survey Science Center
Director Ryozo YOSHINO

Ryozo YOSHINO

Assist. Prof.

Koken OZAKI

Prof.

Takashi NAKAMURA

Visiting Assoc. Prof. Toru KIKKAWA

Assoc. Prof.

Tadahiko MAEDA

Visiting Assoc. Prof. Takahito ABE

Assoc. Prof.

Takahiro TSUCHIYA

Project Researcher

Organization

Prof.

Yutaka UJIIE

Project Researchers
Project Researcher Motoi OKAMOTO

Project Researcher

Eiki TANAKA

Project Researcher Satoko SAITA

Project Researcher

Xiaoling DOU

Center for Engineering and Technical Support
Adjunct Prof.

Makio ISHIGURO

Vice Director

Director               Junji NAKANO

Yoshinori KAWASAKI

Deputy Manager

Yuriko WATANABE

Adjunct Prof.

Yasumasa BABA

Senior Specialist

Saeko TANAKA

Head of Computing Facilities Unit

Kazuhiro NAKAMURA

Head of Information Resources Unit ( Concurrent )Saeko TANAKA

Head of Media Development Unit ( Concurrent )Yuriko WATANABE

Library
Head Junji NAKANO

Administration Planning and Coordination Section
Director Tomoyuki HIGUCHIHead of Planning Unit

Hiroe TSUBAKI

Head of Intellectual Property Unit

Hiroshi MARUYAMAHead of Evaluation Unit

Yoshiyasu TAMURA

Head of NOE Forwarding Unit

Hiroe TSUBAKI

Head of Information and Public Relations Unit

Hiroshi MARUYAMA

Visiting Prof.

Takashi NAMESHIDA

29

Staff
NIPR/ISM Joint Administration OfficeDirector               Tsugio TOKUTA

Director of General Service Center

Akira UCHIYAMA• Planning Section ( ISM ) —————————————————————————————————————————————————————————————————————
Head of Planning Section Yoshiki HAMA
Deputy Head

Mitsuaki OGAWA

Team Leader

Motoyoshi URANO

Specialist

Fumio SUTOTeam Leader

Hiroaki ARAI

Team Leader

Akihiko NAKAMURA• Planning Section ( NIPR) ————————————————————————————————————————————————————————————————————
Head of Planning Section Seiko TOBE
Deputy HeadTeam Leader

Yasuyuki EDURETeam Leader

Nobutaka IRIE

Motokazu TOYODATeam Leader

Hiromi OBAMA

Organization• General Service Center —————————————————————————————————————————————————————————————————————
Deputy Head

Takashi KOSAKATeam Leader

Tomoe HIRANUMATeam Leader

Tomoko IIDA

Deputy Head

Koji SAKAMOTOTeam Leader

Kazuyoshi ASO

Team Leader

Kuniyasu YOKOI

Deputy Head

Toru HAYASHITeam Leader

Yoji ISHII

Council of The Institute of Statistical Mathematics ( As of April 1, 2011)

30

Masanori IYEProfessor, Optical and Infrared Astronomy Division, National Astronomical Observatory of Japan,
National Institutes of Natural Sciences

Yutaka KANO

Professor, Graduate School of Engineering Science, Osaka University

Toshinari KAMAKURA

Professor, Faculty of Science and Engineering, Chuo University

Koji KURIHARA

Executive Vice Director / Professor, Graduate School of Environmental Science, Okayama University

Kunio SHIMIZU

Professor, Faculty of Science and Technology, Keio University

Kazuo SEIYAMA

Professor, Graduate School of Humanities and Sociology, The University of Tokyo

Makoto TAIJI

Core Director,  Computational Biology Research Core,  Quantitative Biology Center (QBiC ),RIKEN Advanced Institute for Computational Science

Nobuhiko TERUIProfessor, Graduate School of Economics and Management, Tohoku University

Yoshihiro YAJIMA

Professor, Graduate School of Economics, The University of Tokyo

Takashi WASHIO

Professor, The Institute of Scientific and Industrial Research, Osaka University

Hiroe TSUBAKI

Professor ( Vice Director-General, ISM )Yoshiyasu TAMURA

Professor ( Vice Director-General, ISM )Hiroshi MARUYAMA

Professor ( Vice Director-General, ISM )Tomoko MATSUI

Professor ( Director of Department of Statistical Modeling, ISM )Takashi NAKAMURA

Professor ( Director of Department of Data Science, ISM )Satoshi KURIKI

Professor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM )Junji NAKANOProfessor ( Director of Center for Engineering and Technical Support, ISM )Shinto EGUCHI

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Kenji FUKUMIZU

Professor ( Department of Statistical Modeling, ISM )Yosihiko OGATA

Professor ( Department of Statistical Modeling, ISM )Nobuhisa KASHIWAGI

Professor ( Department of Data Science, ISM )Organization

Cooperative Research Committee ( As of June 1, 2011)
Akifumi OIKAWA

Professor, The Graduate University for Advanced Studies

Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Toshimitsu HAMASAKI

Associate Professor, Graduate School of Medicine, Osaka University

Yoshinori FUJII

Professor, Faculty of Education and Culture, University of Miyazaki

Yuichi MORI

Professor,  Faculty of Informatics, Okayama University of Science

Tomoko MATSUI

Professor (Director of Department of Statistical Modeling, ISM )Ryozo YOSHINO

Professor (Department of Data Science, ISM )Yoshihiko MIYASATO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Atsushi YOSHIMOTO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Specialist on epidemiology and social researchMichio UMINO

President, Miyagi Gakuin Women’s Unversity

Specialist on epidemiology and social research

Yoshihiko MIURA

President, School of Health and Social Service, Saitama Prefectural University

Person in citizen’s position

Yutaka KURIKI

Principal, Tachikawa City Daiichi Elementary School

Specialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Research education staff of ISM

Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM)

Research education staff of ISM

Hiroe TSUBAKI

Professor ( Vice Director-General, ISM)

Research education staff of ISM

Shigeyuki MATSUI

Professor (Department of Data Science, ISM)

Research education staff of ISM

Tadahiko MAEDA

Associate Professor (Department of Data Science, ISM)

Organization

Research Ethics Review Committee ( As of April 1, 2011)

Professor Emeritus ( As of April 1, 2011)
Kameo MATUSITA

Masakatsu MURAKAMI

Yoshiaki ITOH

Sigeki NISIHIRA

Kunio TANABE

Yasumasa BABA

Tatsuzo SUZUKI

Tadashi MATSUNAWA

Katsuomi HIRANO

Giitiro SUZUKI

Masami HASEGAWA

Masaharu TANEMURA

Ryoichi SHIMIZU

Yoshiyuki SAKAMOTO

Makio ISHIGURO

Noboru OHSUMI

Takemi YANAGIMOTO

31

History

1944

June ● Based on a proposal submitted at an academic study conference in December 1943, the organization
was founded as an institute under the direct control of the Ministry of Education. This proposal aimed
to provide supervision for studies looking into the mathematical principles of probability and theirapplication, and was also intended to facilitate, unify and promote the publication of research results.

1947

April ● The affiliated statistical specialists’ school was opened.
May ● The Institute was divided into the 1st Research Dept. (fundamental theories), the 2nd Research Dept.
(statistical theories for the natural sciences), and the 3rd Research Dept. (statistical theories for thesocial sciences).

1949

June ● The Institute was placed under the control of the Ministry of Education because of the enforcement of
the Ministry of Education Establishment Law.

1955 September ● Reorganized into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (natural and social
science theories), and the 3rd Research Dept. (operations, research, statistical analysis theories). The
laboratory system, comprising 9 laboratories and the research guidance promotion room, was adopted.
1969
1971

October ● A new office building was constructed in Minato Ward.
April ● The 4th Research Dept. (informatics theories) was instituted.

1973

April ● The 5th Research Dept. (prediction and control theories) was instituted.

1975

October ● The 6th Research Dept. (statistical theories of human behavior) was instituted.

1979 November ● The Information Research Building was constructed.

History

1985

April ● Reorganized as an Inter-University Research Institute owing to the revision of the Order for the Enforce­
ment of the National School Establishment Law. The revised law required that the Institute would, asa National Inter-University Research Institute, 1 ) conduct studies on statistical mathematics and its
application, 2 ) provide opportunities for university teachers or other researchers majoring in this field
to utilize the facility, and 3 ) contribute to the development of academic studies in universities.
At the same time, the 6 research departments were reorganized into 4 research departments( Fundamental Statistical Theory, Statistical Methodology, Prediction and Control, and Interdisciplinary
Statistics ). The Statistical Data Analysis Center and the Statistical Education and Information Center
were instituted, and the affiliated Statistical Technician Training Center was abolished.

1988

October ● The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science,part of the Graduate University for Advanced Studies ( SOKENDAI ).

1999

June ● The Institute was reorganized as an Inter-University Research Institute based on the National School
Establishment Law.

1993

April ● The Planning Coordination Chief System was instituted.

1997

April ● The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into theCenter for Information on Statistical Sciences.

2003 September ● The Prediction and Knowledge Discovery Research Center was instituted in the affiliated facility.
2004

April ● The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation of Information and Systems of the Inter-University Research Institute based on the National
University Corporation Law. The Planning Coordination Chief System was abolished and the positionof Vice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathe­
matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Scienceand the School of Multidisciplinary Sciences were instituted.

2005

April ● The research organization was reorganized into three research departments ( the Department of Statis­
tical Modeling, the Department of Data Science, and the Department of Mathematical Analysis andStatistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for
Information on Statistical Sciences, and the Engineering and Technical Services Section were inte­
grated into the Center for Engineering and Technical Support. The affiliated facilities were reorganized
as research departments, and the Risk Analysis Research Center was instituted.

2008

April ● The Research Innovation Center was instituted in the affiliated facility.
The Intellectual Property Unit was instituted.

2009
2010

October ● The ISM was moved to 10-3 Midori-cho, Tachikawa, Tokyo.
June ● Officially opened the Akaike Guest House.
July ● Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch
the General Service Center.
The NOE Forwarding Unit was instituted within the Administration Planning and Coordination Section.

2011

32

January ● Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorail—10 min walk from Takamatsu Sta.
Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/